Job Description
Are you ready to engineer the technological landscape of 2026? OmniVerse Dynamics is seeking a visionary Senior AI & Robotics Engineer to lead our Research and Development division. We are building the infrastructure for a future where autonomous systems and human intelligence merge seamlessly. In this role, you won't just be maintaining existing systems; you will be architecting the next generation of intelligent agents and predictive algorithms that define the era of 2026.
Our mission is to bridge the gap between today’s capabilities and the advanced reality of tomorrow. You will work alongside world-class data scientists and hardware engineers to deploy scalable, ethical, and high-performance AI solutions.
Responsibilities
- Architect Next-Gen AI Models: Lead the design and implementation of advanced neural networks (LLMs, Computer Vision) specifically tailored for complex 2026 use cases.
- Hardware-Software Integration: Collaborate with cross-functional teams to integrate AI logic into next-generation robotics and IoT hardware.
- Optimization & Scalability: Optimize algorithms for real-time processing and edge computing environments to ensure low-latency performance.
- Mentorship & Culture: Mentor junior engineers and conduct rigorous code reviews to foster a culture of technical excellence and innovation.
- Ethical AI Governance: Implement frameworks to ensure AI safety, bias mitigation, and compliance with emerging global standards.
- Trend Analysis: Stay ahead of emerging trends in quantum computing interfaces and synthetic biology to keep OmniVerse at the forefront of the industry.
Qualifications
- Education: Master’s or PhD in Computer Science, Robotics, Cognitive Science, or a related technical field.
- Experience: Minimum of 5 years of experience in AI/ML engineering, with a focus on robotics or autonomous systems.
- Technical Stack: Strong proficiency in Python, PyTorch, TensorFlow, and C++ for performance-critical applications.
- Methodologies: Deep understanding of Reinforcement Learning, Deep Reinforcement Learning (DRL), and Large Language Models (LLMs).
- Infrastructure: Experience with cloud platforms (AWS/GCP), containerization (Docker/Kubernetes), and MLOps pipelines.
- Communication: Excellent problem-solving skills and the ability to present complex technical concepts to non-technical stakeholders.